학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 이재욱.This dissertation aims to conduct the empirical analysis for the financial derivative and cryptocurrency market and to develop analytical techniques based on machine learning models suitable for prediction and estimation of each field.
In the financial derivative market, a Markov chain Monte Carlo (MCMC) methods employ the candidate probability distribution nearest to the target probability distribution to acquire sample distributed from the posterior density. Choice of the candidate probability distribution affects the practical convergence speed of the MCMC methodology and the fitness of the sample. In this dissertation, we propose a MCMC framework possible to samples from the candidate distribution nearest to the target probability density without the specification of the candidate distribution.
We confirm that the jump diffusion models and Bayesian neural networks have the best performance in estimating and predicting given the data of the recent day for the model estimation given S&P index options in 2012. Especially, the jump diffusion model has a very high performance in terms of domain adaptation between the American option and the European option. This difference is reflected in the fact that the jump diffusion model is based on the common asset of the American option and the European option.
Based on this empirical precedent study, we proposed a machine learning model called generative Bayesian neural network (GBNN) to overcome the disadvantages of the machine learning model. GBNN maximizes posterior probability through the GBNN obtains prior information from the GBNN data learned up to the previous day, and learns likelihood probability from actual trading data of learning day. We identify that the GBNN model outperform other benchmark models in terms of model prediction.
Bitcoin is a successful cryptocurrency, and it has been extensively studied in fields of economics and computer science. In this dissertation, we analyze the time series of Bitcoin price with a BNN using Blockchain information in addition to macroeconomic variables. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the Bitcoin price in Aug. 2017.
In addition, we suggested the enhanced GRU model for correlation analysis between cryptocurrency markets. Assuming that the gate value obtained from the GRU model is the parameter of the VAR model, it makes possible to visualize the correlation between various alternative currencies in the cryptocurrency market. As a result, it is confirmed that there is a very significant correlation between the currencies separated from the existing currencies and the existing currencies.Chapter 1 Introduction 21
1.1 Financial derivative market analysis 21
1.2 Cryptocurrency market analysis 24
1.3 Aims of the Dissertation 26
1.4 Outline of the Dissertation 28
Chapter 2 Literature Review 29
2.1 Review of Financial Econometric Models 29
2.1.1 Time series models 29
2.1.2 Option pricing methods 34
2.2 Review of Statistical Machine Learning Models 39
2.2.1 Articial neural networks 39
2.2.2 Bayesian neural networks 39
2.2.3 Support vector regression 43
2.2.4 Gaussian process 45
Chapter 3 Predictive Models for the Derivatives Market 47
3.1 Chapter Overview 47
3.2 A Generative Model Sampler for Inference in State Space Model 51
3.2.1 Backgrounds 51
3.2.2 Proposed methods: generative model sampler 56
3.3 Machine Learning versus Econometric Models in Predictability of Financial Options Markets 59
3.3.1 Data description and experimental design 59
3.3.2 Estimation and prediction performance 62
3.3.3 Robustness and Domain Adaptation Performance of the Models 66
3.4 A Generative Bayesian Neural Networks Model for Risk-Neutral Option Pricing 70
3.4.1 Proposed method 70
3.4.2 Empirical Studies 74
3.5 Chapter Summary 86
Chapter 4 Predictive Models for Blockchain and Cryptocurrency Market 89
4.1 Chapter Overview 89
4.2 Economics of Bitcoin and Blockchain 91
4.3 An Empirical Study on Modeling and Prediction of Bitcoin Prices Based on Blockchain Information 93
4.3.1 Data Specication and Structure of the Experiment 93
4.3.2 Linear Regression Analysis 99
4.3.3 Estimation and Prediction Results of Bitcoin Price 104
4.4 Enhanced GRU Framework for Correlation Analysis of Cryptocurrency Market 111
4.4.1 Enhanced GRU Framework 111
4.4.2 Empricial Studies 113
4.5 Chapter Summary 115
Chapter 5 Conclusion 119
5.1 Contributions 119
5.2 Future Work 122
Bibliography 125
국문초록 161Docto
